Nature Neuroscience

Chronic pain mechanisms are complex, spanning multiple brain regions and networks. We ask whether resting brain activity carries a readout of that state. From a few minutes of resting-state electroencephalography (EEG), we generate a spectrogram to represent how each region of the cortex oscillates across frequency and time and pass it through CREST (Cortical Resting-state EEG Spatial Transformer): a frozen image-recognition network that reads each region as an image--here, a spectrogram--paired with a graph model that weighs the 56 cortical regions together to classify chronic-pain status. Across 125 people (74 with chronic pain, 51 healthy controls), evaluated through a leave-one-subject-out cross-validation, CREST separates the two groups with an area under the receiver operating characteristic curve (AUROC) = 0.782 (permutation p < 0.005). Control experiments implicate each persons individual alpha rhythm. Clinical relevanceA resting-state EEG readout of chronic MSK pain could clarify pathophysiology and inform treatment.

Metabolic psychiatry has recently achieved unprecedented clinical rescue in treatment-resistant Schizophrenia (SCZ) utilizing targeted ketogenic interventions. However, the field has operated without a defined genomic anchor, leaving the biophysical mechanism of these therapies largely unexplained. Here, we report the discovery of the definitive metabolic sensor array driving this pathology. By integrating high-resolution topological mapping of SCZ GWAS summary statistics, 3D chromatin conformation (Hi-C), and multi-tissue transcriptomics, we identify massive, non-coding structural variances flanking the HCAR2/HCAR1 tandem locus--the brain's master thermodynamic governor. We demonstrate that while the protein-coding hardware of these receptors remains intact, their shared 3D Topologically Associating Domain (TAD) is fundamentally fractured. This structural collapse drives a perfect transcriptomic double dissociation in the human cortex: the 3' mutational "skyscraper" severely downregulates the HCAR1 lactate emergency brake, while the 5' mutational cluster selectively paralyzes the HCAR2 beta-hydroxybutyrate (BHB) and niacin cooling switch. This dual-flank enhancer failure elegantly provides a definitive genomic etiology for historical SCZ biomarkers, physically explaining both chronic cerebrospinal fluid lactate pooling and the infamous "absent niacin flush." Furthermore, peripheral eQTL mapping reveals profound antagonistic pleiotropy, characterized by a hyper-activation of the HCAR1 lactate shuttle in the testis, explaining the evolutionary conservation of this metabolically catastrophic architecture. Ultimately, we reframe Schizophrenia not as an intrinsic neurological defect, but as an evolutionary "fuel mismatch." The high-performance cognitive architecture of the hominid brain, evolved for ancestral ketogenic environments, experiences a catastrophic thermodynamic crash when deprived of its requisite BHB coolant by modern, high-glycemic diets.

The episodic memory system provides humans with a unique ability to form and retrieve rich and detailed memories. This capacity requires representing temporal context at encoding and recovering it at retrieval to drive successful recall. Time cells and ramping cells in human medial temporal cortex have been proposed as substrates of temporal context, but whether they participate in context recovery during retrieval remains unknown. Using microelectrode recordings from neurosurgical patients across two classical episodic paradigms, free recall and serial reconstruction, we identify time sensitive neurons and demonstrate that these neuron populations participate in neuronal assemblies and organized sequences on gamma and theta time scales respectively. Consistent with recovery of temporal context predicted by behavioral models of episodic processing, time cell firing precedes the broader assembly population and initiates sequential activation retrieval. These properties predict contextually-mediated recall behavior. We also identify phase coding by complementary populations as convergent mechanisms whose recruitment depends on task demands. Finally, we demonstrate the emergence of context--sensitive neurons in a naturalistic viewing dataset lacking the predictable temporal scaffold of canonical assays. These findings identify cellular mechanisms by which the human medial temporal cortex represents temporal context during episodic encoding and recovers it during retrieval.

The genetic basis of extreme cognitive plasticity in human innovators remains poorly characterized. Here, we demonstrate that the genomic architecture underlying scientific innovation significantly overlaps with the polygenic risks for neuropsychiatric phenotypes, such as schizophrenia. Utilizing a comparative genomic approach across psychiatric cohorts (PGC) and the UK Biobank (Scientific Creativity), we identify a coordinated "Vanguard Engine" consisting of hyper-tuned voltage-gated calcium channels (CACNA1C, CACNB2), glutamatergic receptors (GRIN2A), synaptic plasticity regulators (BDNF), and the core linguistic-symbolic coordinator (FOXP2). We posit that these variants establish a high-voltage neural environment optimized for associative divergence. Furthermore, we characterize the PDHB and HCAR2 loci as critical metabolic governors against thermal overload and identify structural variance in HDAC2 as the epigenetic clamp linking ancestral fuel availability ({beta}-hydroxybutyrate) directly to the transcriptional control of this plasticity network. We conclude that psychiatric pathology is an emergent property of a fuel-mismatch, wherein a high-performance cognitive architecture is sustained by a carbohydrate-heavy diet, leading to systemic thermodynamic failure. This framework provides a unified, mechanistic explanation for the spectrum between extreme cognitive innovation and pathological collapse.

How sensory cortical activity is read out by downstream circuits to guide behavior is a fundamental unsolved problem. Substantial work has examined correlations between sensory neural responses and animals perceptual judgments, but their interpretations remained controversial due to many intervening variables, such as responses of unrecorded neurons. Furthermore, stimulus and choice encoding in sensory populations are often not well aligned, and it remains contested whether this misalignment indicates a limitation in sensory readout. Here, we introduce a closed-loop, neurofeedback paradigm that directly interrogates the capacity of sensory readout: the key idea is to train subjects to report specific patterns of population activity in a sensory area recorded online, rather than the actual stimuli presented. To test this, we trained macaque monkeys on a visual change-detection task using shape stimuli, implanted an electrode array in visual area V4, and tested whether they could be further trained to rely on their own V4 activity along specific axes in neural state space. Strikingly, monkeys successfully increased the neuron-choice correlation along trained axes in neural population state space. No detectable changes in stimulus selectivity or noise correlations were found within the recorded population, and further model simulations confirmed that adjustment of sensory readouts best accounted for the results. A control experiment that merely disrupted the stimulus-reward contingency without a closed loop failed to enhance neuron-choice correlation. Together, these results demonstrate that closed-loop neural feedback achieves neuron-choice alignment beyond the ceiling of natural perceptual training, suggesting that the misalignment in perceptual tasks reflects constraints on learning within naturally available training regimes.

Preprints now disseminate a large share of biomedical research before peer review. Because they have not yet passed peer review, some scientists regard preprint claims as unverified or potentially unreliable, yet how much those claims change before publication has so far been quantified only in smaller cohorts, with results that vary by field and topic. Here, we compiled every bioRxiv preprint posted between 2018 and 2025 that we could match by DOI to a peer-reviewed published version, yielding 72,644 preprint-publication pairs. Using a large language model (Claude Sonnet 4.6), we parsed every preprint-publication abstract pair into one primary and two secondary claims, and classified each pair for content change (unchanged, minor, major) and hedging shift (more cautious, more confident, unchanged). On a validation subsample, the model agreed with two independent domain experts about as well as the experts agreed with each other (Cohens kappa 0.63 to 0.66). The primary claim was unchanged in 39.9% of abstracts, minorly revised in 50.0%, and substantially revised in only 10.2%. Hedging shifts were uncommon and asymmetric, with twice as many claims becoming more cautious as more confident (8.4% vs 4.2%). Major revisions were more frequent after long peer review (14.1% in the slowest versus 7.0% in the fastest tertile of review time) and declined over the study period (17.0% in 2019 to 5.7% in 2024). Over the same period, biomedical papers that were never posted as preprints were retracted at roughly twice the rate of those that were. Together, these data show that the move from preprint to peer-reviewed publication leaves the central claims of most biomedical abstracts intact, indicating that preprints are a reliable source of biomedical research.

Acquired central nervous system (CNS) injury is one of the most common neurological conditions globally, yet effective treatment options are lacking. As the main tissue-resident macrophages of the CNS, microglia have emerged as key functional regulators of CNS repair. However, means to induce neuroprotective microglia and harness their intrinsic repair capabilities have remained elusive. Here we identify gp130 as a key receptor molecule for facilitating bidirectional microglia-neuron communication that improves outcomes from CNS injury. We show that activation of gp130 in CNS-resident microglia triggers the secretion of leukemia inhibitory factor (LIF), a neurotrophic cytokine. LIF induces neuronal IL-6 secretion that then acts back onto the microglial gp130 receptor, thus creating a neuroprotective loop. We demonstrate the broad therapeutic potential of acute gp130 activation across multiple models, including traumatic brain injury, stroke, and spinal cord injury, and that this pathway can be leveraged therapeutically with designer cytokines.

Adaptive behavior requires external input to be interpreted together with internal state and ongoing behavior. During pain, noxious somatosensory input evokes movement and arousal, and injury reshapes this relationship, yet how cortical activity organizes stimulus content with behavioral state remains unclear. In awake mice, we delivered hindpaw stimuli while tracking movement, arousal and facial expression, and studied primary somatosensory cortex (S1) using widefield and two-photon calcium imaging, single-action-potential activation of nociceptors, and S1 silencing. Here, we show that S1 neurons were broadly recruited by stimulus and state, whereas latent population dimensions carried mechanical stimulus content. Inflammatory injury caused a reorganization of S1 geometry, binding state and protective responses tighter together. Noxious heat drove S1 as strongly, but engaged mostly the state axis. S1 silencing reduced mechanical hypersensitivity, arousal, and facial expressions. S1 thus embeds mechanical input within a stimulus-state geometry, which is reorganized during inflammatory injury to support adaptation of a coordinated protective response.

Neural recordings from different individuals vary substantially even when behavior is broadly shared. The sampled neurons differ, and the same behavior occurs at different times. Standard cross-subject analyses rely on matched time points or anatomical correspondence, which excludes many datasets. We recently introduced Shared Representation Discovery (ShaReD), which identifies neural-behavioral relationships conserved across subjects by learning a shared behavioral projection together with subject-specific neural projections. Here we develop and benchmark this method using synthetic, primate, and rat data. On synthetic data, ShaReD recovers common structure across noise levels, sample sizes, and subject counts, and separates components confined to different groups of subjects. In non-human primate motor cortex, ShaReD identifies kinematic representations that generalize across individuals and across reaching tasks with different movement statistics. In rats navigating a spatial alternation task, ShaReD isolates behavior-aligned directions within the CA1-to-PFC communication subspace. ShaReD thus extends multi-subject analysis to datasets in which comparable behaviors occur without cross-subject temporal correspondence.

Memory consolidation during sleep requires coordinated reactivation of specific experiences across hippocampus and cortex. This process occurs with synchronized neural oscillations including cortical slow waves, thalamocortical spindles, and hippocampal sharp-wave ripples. While temporal coupling of these rhythms is implicated in consolidation, a fundamental question remains: do oscillations reflect a general increase in communication, or do they selectively coordinate which memories are reactivated together across regions? Here we segregated competing memory representations in primary visual cortex by training mice on two visually distinct virtual reality tracks, each restricted to one visual hemifield, rendering their representations lateralized in cortex. Using large-scale electrophysiology, we demonstrate that hippocampus and cortex coherently reactivate the same memories. Temporally, the cortical memory trace active before and after a ripple is coherent with the memory hippocampus reactivates. Crucially, this reactivation coherence is maximally enhanced by high hippocampal ripple power in concert with high cortical spindle-band power and slow oscillation trough phase localized to the dominant memory trace reactivating. Our findings establish that sleep oscillations coordinate content-specific cortico-hippocampal reactivation necessary for consolidation.

Recent studies report rotational population dynamics in spinal cord neuronal activity during rhythmic movements, suggesting computational principles shared with motor cortex. Here we show that primate cervical spinal cord activity does not exhibit rotational dynamics during an alternating single-joint isometric wrist task, instead it displays low-dimensional alternating population patterns. Positive controls confirm presence of rotational structure in motor cortex activity during the same task, indicating distinct computational strategies across the motor axis. Cortical neurons with post-spike effects on motoneurons had activity with dynamics resembling cortical rather than spinal populations.

Open data sharing is increasingly mandated by research funders, journals, and institutions, yet large-scale compliance measurement remains challenging. We analyzed 951,949 open access biomedical research articles published between January 2024 and June 2025 using a dual-source pipeline: PDF-based text extraction (MinerU) where PDFs were available and PMC XML otherwise, followed by algorithmic detection of data sharing statements (oddpub v7.2.3), enriched with funder, journal, and institutional metadata from OpenAlex. The corpus included 294,172 PDF-covered articles (30.9%) and 657,777 XML-only articles (69.1%). We found an over-all open data rate of 8.7%, rising to 11.7% among funder-linked articles (those with at least one funder identified in the metadata). Rates varied more than tenfold across the research ecosystem: leading major funders reached observed open data rates of 20-24%, while top journals reached observed rates of 70-86%, with corrected estimates as high as 92.9% (Nature Genetics) after adjusting for XML-only coverage limitations. PDF-based detection identified approximately 52% more data sharing statements than XML-based methods on the same articles. These observed rates differ markedly across funders and journals, and current overall sharing remains far below universal compliance. These patterns provide an empirical baseline against which future policy changes can be measured. An interactive dashboard at https://www.opensciencemetrics.org enables stakeholders to explore and benchmark these results.

Every brain must adapt to an unpredictable world, yet individuals differ in how readily they learn. Theoretical work suggests that learning is fastest when a system, whether biological or synthetic, is initialized in a state close to instability - i.e., near criticality - because critical dynamics are imbued with a diverse repertoire of patterns and multi-scale correlations. Here, we empirically estimate distance to criticality in the brain and show that it predicts the rate of adaptability underlying learning, neuronal tuning, and general intelligence. In mouse motor cortex, proximity to criticality forecasts learning rate of two future complex tasks: prey capture hunt and ladder crossing. In contrast, distance to criticality predicted neither an animal's naive ability nor its asymptotic skill - isolating the rate of learning itself. In visual cortex of young ferrets, proximity to criticality predicts how strongly experience reshapes neural tuning. In human frontal cortex, it correlates with general cognitive ability. A minimal recurrent network model reproduced these results and offers a mechanism: proximity to criticality defines the timescale over which a system can learn from its past experiences, directly setting the rate of learning. A single dynamical property can account for the capacity to learn, from artificial networks to the mammalian brain.

Learning is thought to arise from synaptic modifications embedded in brain-wide circuits 1-3, yet how such circuits coordinate plasticity to support complex behaviour is not known 4,5. Inspired by deep learning, we propose a theory in which pathway-specific cortical feedback drives dendrite-dependent burst plasticity across cortical hierarchies. We show that this mechanism enables online hierarchical credit assignment and learning of complex image recognition and reward-driven tasks. This theory links credit assignment to cell-type-specific control of dendritic excitation-inhibition balance. In doing so, it provides a unified account of cell-type-specific modulation of synaptic plasticity, learning-dependent changes in interneurons, and neuron-specific dendritic error signals. The theory further predicts that interneurons constrain the dimensionality of error-related feedback, offering a functional rationale for cortex-wide gradients in interneuron density. Taken together, these findings indicate that distinct cortical cell types jointly coordinate learning across hierarchical circuits, connecting synaptic plasticity, circuit-level computation, and behaviour.

Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have more limited utility for treating chronic pain. By comparison, electrical or magnetic stimulation of the motor cortex can induce pain relief lasting weeks, for which the underlying mechanisms have remained unclear. Here we report an unconventional role for endogenous opioidergic signaling in the rapid induction of long-lasting analgesia from motor cortical stimulation, which triggers opioid-peptide-dependent neural plasticity in the rostral ventromedial medulla (RVM), a key node in the brain's descending pain control pathways. To dissect the circuit and cellular bases for these effects, we created a miniaturized, millimeter-sized device allowing focal, non-invasive transcranial magnetic stimulation (TMS) of the mouse motor cortex. In mice with chronic neuropathic pain, reflexive and affective pain behaviors diminished for 1-2 weeks after one session of TMS treatment. Chemogenetic and optogenetic manipulations showed that motor cortical layer 5 pyramidal neurons with axonal projections to the RVM mediated TMS-induced pain relief. High-density electrophysiological recordings revealed that TMS treatment shifted the balance of RVM activity between pain-ON and pain-OFF neurons to a state promoting greater suppression of pain. Genetic and neuropharmacological manipulations revealed that NMDA-receptor-dependent signaling and MOR activation by endogenous opioid peptides in the RVM jointly mediate the long-lasting analgesia induced by a transient bout of TMS. Strikingly, enkephalinase inhibition in the RVM during TMS treatment enhanced the amplitude and duration of analgesia, showing that transiently boosting endogenous opioidergic signaling during TMS increases analgesia-conferring plasticity. In accord, re-analyses of data from human subjects with chronic pain support the idea that opioid administration amplifies analgesia from motor cortical TMS. Overall, our results showcase miniaturized TMS devices as versatile tools for basic and translational neuroscience and detail a hybrid, long-range neural network and NMDA- and opioid-receptor-dependent plasticity mechanism for durable pain relief. These findings point the way to mechanistically grounded, synergistic neurostimulation and drug therapies for brain diseases and disorders that jointly target neural circuit and molecular signaling pathways.

Regeneration of central nervous system (CNS) axons depends on Transcription Factors (TFs) that reactivate developmental growth programs, yet most such factors remain unknown. By intersecting developmental chromatin binding with pro-growth gene networks, we identified two retinoic acid receptor transcription factors, RARA and RARG, whose occupancy at growth-associated genes is progressively lost as neurons mature. Restoring both factors together increased neurite outgrowth beyond either alone in two independent systems, the Neuro-2a cell line and primary cortical neurons. In vivo, the same combination drove cross-midline sprouting after pyramidotomy and long-tract regeneration after thoracic spinal cord crush, with concordant recovery of hindlimb gait and grip strength. Interestingly, neither receptor alone was sufficient, hinting at combinatorial regulation. Single-nucleus transcriptomics delineated that only the combination reactivated relevant cytoskeletal and gene-expression programs, while genome-wide binding maps showed that RARA and RARG partition the regulatory landscape, with RARG dominating promoters and RARA occupying distal enhancers, so that neither receptor reconstitutes the developmental growth state alone. Intriguingly, this cooperative requirement was specific to the CNS: in peripheral sensory neurons, RARG alone was sufficient and RARA was dispensable. These data identify RARA and RARG as novel cooperative regulators of regenerative axon growth in mammalian CNS and PNS neurons and potential targets for therapeutic intervention.

Leading deep neural network encoding models predict visual cortical responses with nearly indistinguishable accuracy, raising the strong inference that these models have converged on the same underlying brain-aligned parameterization of natural image space. Here we demonstrate that this is not the case. We introduce axis-aligned feature accentuation, which converts each model's fitted encoding axis into graded stimulus perturbations that are predicted to parametrically control neural firing within and beyond the natural-image range. We generated over 27,500 controller stimuli from ten leading vision models and presented them to five macaques in closed-loop experiments targeting early, mid-, and high-level visual areas. Despite matched natural image predictivity, models diverged strongly in their ability to control neural firing using accentuated stimuli, revealing that most model encoding axes failed to capture the precise tuning of their corresponding neurons. The two adversarially trained models showed a consistent advantage, though adversarial robustness was only weakly predictive of neural control across other models. Instead, control was better predicted by the spatial frequency structure of the input gradient: the distribution of pixels influencing each encoding axis. Overall, these results establish neural control via axis-aligned feature accentuation as a causal method to assess the alignment between how neurons and models parameterize the visual world.

The ability to delay gratification emerges early in life and is linked to long-term health and economic success. Conversely, high impulsivity, marked by a preference for immediate rewards, can be associated with psychiatric disorders. Although processes underlying human delay discounting have been studied at behavioural and macroscopic neural levels, they remain elusive at the single-neuron level. Specifically, it is unknown how human neurons encode extended delays and predict intertemporal choices, and how these processes are impacted by impulsivity. Here, we record single-neuron activity in the human medial temporal lobe (MTL) to explore decision and delay coding. We identify neurons that predict upcoming decisions in the amygdala and hippocampus. Neurons in the entorhinal cortex and hippocampus encode reward delays, with particularly hippocampal population activity coding prospective temporal periods. Importantly, neuronal activity in impulsive individuals shows diminished prospective temporal coding and predicts decisions only shortly before choices are reported. Our findings reveal how distinct MTL regions contribute to intertemporal decisions and provide insight into the neuronal signatures underlying impulsivity.

Alzheimers disease (AD) features amyloid-{beta} plaques and tau pathology, yet the mechanism underlying early and clinically significant myelin loss remains unresolved. Here, we report human iPSC-derived forebrain organoids with doxycycline-inducible expression of SOX10, OLIG2, and NKX6-2 (SON), which generate robust, mature oligodendrocytes and compact myelin in vitro and in vivo. Introducing amyloid precursor protein (APP) pathogenic mutations produces extracellular amyloid-{beta} plaques and phosphorylated tau, accompanied by reduced myelin basic protein (MBP) expression and disrupted myelin ultrastructure. Single-cell and spatial transcriptomics combined with amyloid plaque imaging reveal a plaque density-dependent oligodendrocyte transcriptional reprogramming that coordinately induces immune activation, calcium signaling, lipid remodeling, and proteasomal subunit remodeling, a program incompatible with MBP protein accumulation. This program is conserved in human AD postmortem tissues, implicating proteostatic disruption as a mechanism underlying the transcript-protein disconnect and myelin loss in AD.

Major depressive disorder (MDD) is a complex psychiatric disorder affecting millions of individuals worldwide. Astrocytes, which have been implicated in MDD by several studies, are the most abundant non-neuronal cells in the brain and play critical roles in synaptic regulation, blood-brain barrier maintenance, and immune modulation. While astrocytic molecular and morphological abnormalities are well-established features of MDD, these alterations have not been resolved within their spatial context. Here, we combine spatial transcriptomics with matched snRNA-seq and snATAC-seq datasets to spatially map molecularly distinct astrocyte subtypes and define their regional contributions to MDD pathology. This spatial context further enables the characterization of astrocyte interactions with neighboring cell populations, providing a more holistic assessment of how dysfunctional astrocytes influence local brain microenvironments and circuit function in MDD. We identified spatially localized astrocytic dysfunction in deep cortical layers of the MDD dlPFC, converging across transcriptomic, chromatin, and spatial modalities and centering on the PSAP-GPR37L1 signaling axis. Together, these findings identify astrocyte dysfunction as a key feature of MDD and demonstrate the value of spatially resolved molecular profiling for uncovering how altered astrocyte-neuron communication within deep cortical layers may contribute to disease pathology.